Proteomic profiling reveals engineered chitosan nanoparticles mediated cellular crosstalk and immunomodulation for therapeutic application in apical periodontitis
Bibliographic record
Abstract
Macrophages (MQ) are major constituents of chronically inflamed periapical tissues in apical periodontitis. This study aimed to investigate the immunomodulatory effect of engineered bioactive chitosan-based nanoparticles (CSnp) antibiofilm medication on MQ cocultured with periodontal ligament fibroblasts (PdLF). Cells viability, spreading, PdLF migration, and intracellular CSnp uptake were characterized. Tandem Mass Tag-based proteomics was applied to analyze MQ global protein expression profiles after interaction with Enterococcus faecalis biofilm, CSnp-treated biofilm, and CSnp. Secreted inflammatory mediators were analyzed. Following bioinformatics analyses, candidate proteins were validated via targeted proteomics. CSnp maintained cells viability, increased MQ spreading, and PdLF migration (p < 0.05). Transmission electron micrographs demonstrated CSnp internalization via macropinocytosis, clathrin-mediated endocytosis, and phagocytosis. Proteomic analysis revealed that CSnp-treated biofilm upregulated proteins (>1.5-folds, p < 0.05) showed functional enrichment in the pathway of metal sequestration by antimicrobial proteins, while downregulated proteins showed enrichment in ferroptosis. CSnp upregulated proteins exhibiting antioxidant and immunoregulatory properties. Upregulation of SERPINB1 by CSnp (>1.5-folds, p < 0.05) was validated. CSnp-treated biofilm reduced pro-inflammatory IL-1β and nitric oxide but enhanced anti-inflammatory IL-10 and TGF-β1 (p < 0.05). Internalized engineered bioactive CSnp reprogrammed MQ proteomic and cytokine profiles to modulate biofilm-mediated inflammation, and prompted PdLF migration, emphasizing its potential to regulate healing process in the treatment of apical periodontitis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".